Application of Eigenvector Spatial Filtering in Spatial Analysis of Larceny-theft
In spatial analysis and modeling of urban crime,the spatial autocorrelation of model residuals poses an significant obstacle to model parameter estimation and produces deviations in analysis of the determinants of urban crime. The presence of significant spatial autocorrelation of model residuals and overdispersion of the model could lead to biased estimates and misleading inferences,even resulting in wrong conclusions. This study employed a new spatial regression method,namely Poisson regression with Eigenvector Spatial Filtering,to solve the problem of model residual spatial autocorrelation and model overdispersion to avoid subsequent biased estimation in model results. To explain the spatial variation of urban crime,we used two theories in spatial crime analysis:crime pattern theory and social disorganization theory. The case study focused on the main urban area of the Haining city in Zhejiang province,China,and the crime data that we used were larceny-theft over a four-year period,from January 2018 to September 2021. Other datasets that we employed for generating covariates in-cluded POI data of various facilities in Haining,the Luojia 1-01 nighttime light data,and the WorldPop global population data. We established a Poisson regression model with eigenvector spatial filtering and further identi-fied several important determinants of larceny-theft with unbiased model parameters. The major findings are as follows:(1) The Poisson regression with eigenvector spatial filtering identified the spatial autocorrelation of model residuals,ensuring no significant spatial autocorrelation issue in model residuals. This can improve the model's goodness of fit,correct model parameter estimation,alleviate the impact of overdispersion,and retrieve omitted variables. More importantly,the eigenvector spatial filtering method could be applied to other general-ized linear models such as Poisson regression;(2) The results of Emerging Hot Spot Analysis showed that the ab-solute number of larceny-theft decreased during the period of COVID-19 pandemic,and crime hot spots oc-curred in the central places of the main urban area of Haining while the cold spots exhibited a trend of multipoint distribution;(3) The level of relative deprivation measured by per capita nighttime light had a significant impact on larceny-theft in the unbiased model with eigenvector spatial filtering;(4) The crime generator,attractor and enabler in various built environment of interest had a significant impact on larceny-theft. The inconsistencies with the conclusions of previous studies were also discussed.
eigenvector spatial filteringspatial analysisPoisson Regressionlarceny-theftbuilt environmentsocioeconomic deprivationemerging hot spots analysis
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